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Genes 2018, 9(3), 139; https://doi.org/10.3390/genes9030139

A Semi-Supervised Learning Algorithm for Predicting Four Types MiRNA-Disease Associations by Mutual Information in a Heterogeneous Network

School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
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Received: 31 December 2017 / Revised: 20 February 2018 / Accepted: 22 February 2018 / Published: 2 March 2018
(This article belongs to the Section Technologies and Resources for Genetics)
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Abstract

Increasing evidence suggests that dysregulation of microRNAs (miRNAs) may lead to a variety of diseases. Therefore, identifying disease-related miRNAs is a crucial problem. Currently, many computational approaches have been proposed to predict binary miRNA-disease associations. In this study, in order to predict underlying miRNA-disease association types, a semi-supervised model called the network-based label propagation algorithm is proposed to infer multiple types of miRNA-disease associations (NLPMMDA) by mutual information derived from the heterogeneous network. The NLPMMDA method integrates disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity information of miRNAs and diseases to construct a heterogeneous network. NLPMMDA is a semi-supervised model which does not require verified negative samples. Leave-one-out cross validation (LOOCV) was implemented for four known types of miRNA-disease associations and demonstrated the reliable performance of our method. Moreover, case studies of lung cancer and breast cancer confirmed effective performance of NLPMMDA to predict novel miRNA-disease associations and their association types. View Full-Text
Keywords: multiple type miRNA-disease association prediction; semi-supervised learning; network similarity; label propagation algorithm multiple type miRNA-disease association prediction; semi-supervised learning; network similarity; label propagation algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zhang, X.; Yin, J.; Zhang, X. A Semi-Supervised Learning Algorithm for Predicting Four Types MiRNA-Disease Associations by Mutual Information in a Heterogeneous Network. Genes 2018, 9, 139.

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